Introduction

In addition to SIP, we also ran a simple enrichment experiment, adding one 12C substrate to smaller microcoms containing ~ 5g soil. Unlike the SIP experiment, only one substrate was added to each microcosm and this time we used soil from all 10 locations, not just Monkey Run. Also we only sampled microcoms on two days for each substrate:

  • Days 2 and 4: xylose, amino acids, and vanillin
  • Days 14 and 28: cellulose and palmitic acid

We also had water treated controls for each timepoint as something to compare against.

In this notebook I will examine these samples both on their own and in relation to the incorporators identified in the MW-HR-SIP analysis.

Initialization

# For data handling
library(dplyr)
library(phyloseq)

# For analysis
library(vegan)
library(nlme)
library(lsmeans)

# For plotting
library(ggplot2)

# Set color schemes
eco.col = c(agriculture="#00BA38", meadow="#619CFF", forest="#F8766D")

g_legend<-function(a.gplot){
  tmp <- ggplot_gtable(ggplot_build(a.gplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)}

Data

The OTU abundances and phylogeny can be found in the master phyloseq object.

# Import phyloseq object
physeq = readRDS("/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_backups_8_8_19/phyloseq/fullcyc2physeq.RDS")

# Subset to just the unfractionated samples and remove the controls
enr.physeq = subset_samples(physeq, exp_type == "Enrichment" & sample_type == "unknown")
bulk.physeq = subset_samples(physeq, exp_type == "bulk" & sample_type == "unknown")
physeq = NULL

# Remove non-bacteria (aka Archaea)
enr.physeq = subset_taxa(enr.physeq, Domain == "Bacteria")
bulk.physeq = subset_taxa(bulk.physeq, Domain == "Bacteria")
  
# Add in a different phylogenetic tree. The one in the phyloseq might be an older version.
tree = read_tree("/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_backups_8_8_19/fullcyc2.bacteria.cogent.tree")
phy_tree(enr.physeq) = tree
phy_tree(bulk.physeq) = tree

# Remove any OTUs no longer found in the samples
enr.physeq = prune_taxa(taxa_sums(enr.physeq) > 0, enr.physeq)
bulk.physeq = prune_taxa(taxa_sums(bulk.physeq) > 0, bulk.physeq)

Incorporator status can be found in the log2 fold change dataframe.

# Get the l2fc dataframe and add in columns indicating the land-use, labeled substrate, and day
l2fc.df = readRDS(file = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_l2fc_testoutput.rds") %>%
  mutate(ecosystem  = factor(gsub(".+ecosystem == [ \']*([A-z]+).+", "\\1", .id),
                             levels = c("agriculture", "meadow", "forest")),
         day  = gsub(".+day == [ \']*([0-9]+).+", "\\1", .id),
         substrate = factor(gsub(".+(13C-[A-z]+).+", "\\1", .id),
                            levels = c("13C-Xyl", "13C-Ami", "13C-Van", "13C-Cel", "13C-Pal")))

For many of the following analyses I want a rarefied OTU table. This is one way to correct for differing sequencing depths across all my samples. I will set the seed for this process so that I can replicate this analysis if necessary (seed = 4242).

enr.rare.physeq = rarefy_even_depth(enr.physeq, rngseed=4242)
bulk.rare.physeq = rarefy_even_depth(bulk.physeq, rngseed=4242)

unique(colSums(otu_table(enr.rare.physeq)))
## [1] 14503

Basic stats

print(paste("Maximum read count =", max(colSums(otu_table(enr.physeq)))))
## [1] "Maximum read count = 194737"
print(paste("Minimum read count =", min(colSums(otu_table(enr.physeq)))))
## [1] "Minimum read count = 14503"
print(paste("Rarefied read count =", unique(colSums(otu_table(enr.rare.physeq)))))
## [1] "Rarefied read count = 14503"
print(paste("Number of OTUs total =", ntaxa(enr.physeq)))
## [1] "Number of OTUs total = 28296"
print(paste("Number of OTUs rarefied =", ntaxa(enr.rare.physeq)))
## [1] "Number of OTUs rarefied = 24659"
print(paste("Number of phyla total =", length(unique(filter(data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE), !(is.na(Phylum)))$Phylum))))
## [1] "Number of phyla total = 45"
print(paste("Number of phyla total =", length(unique(filter(data.frame(tax_table(enr.rare.physeq), stringsAsFactors = FALSE), !(is.na(Phylum)))$Phylum))))
## [1] "Number of phyla total = 45"

Diversity differences between treatment and control microcosms

As with the SIP enrichments I want to see if there carbon addition leads to a shift in diversity.

Here I’ll calculate the desired alpha diveristy measures.

OTU.table = t(otu_table(enr.rare.physeq))
alpha_div.df = data.frame(X.Sample = rownames(OTU.table), 
                          richness = specnumber(OTU.table),
                          shannon = diversity(OTU.table, index="shannon"),
                          simpson = diversity(OTU.table, index="simpson")) %>%
  mutate(evenness = shannon/log(richness)) %>%
  left_join(data.frame(sample_data(enr.rare.physeq)) %>% select(X.Sample, ecosystem, substrate, day, location),
            by = "X.Sample") %>%
  mutate(ecosystem = factor(ecosystem, levels = c("agriculture", "meadow", "forest")),
         substrate = factor(substrate, levels = c("H2O-Con", "12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
         time_point = ifelse(day %in% c(2, 14), "Early", "Late")) %>%
  tidyr::gather(key="method", value="Treatment", 
                -X.Sample, -ecosystem, -substrate, -day, -location, -time_point)

alpha_div_H2O.df = alpha_div.df %>%
  filter(substrate == "H2O-Con") %>%
  rename(Control = Treatment) %>%
  select(ecosystem, location, day, method, Control)

alpha_div_treat.df = alpha_div.df %>%
  filter(substrate != "H2O-Con") %>%
  select(-X.Sample) %>%
  left_join(alpha_div_H2O.df, by = c("ecosystem", "location", "day", "method")) %>%
  tidyr::gather(key = "treatment", value = "diversity", -ecosystem, -substrate, -day, -location, -time_point, -method) %>%
  arrange(ecosystem, substrate, day, location)

Early timepoints

Early timepoints are the first timepoint for each of the substrates (Day 2 for Xylose, amino acids, and vanillin; Day 14 for palmitic acid and cellulose). This is similar to the early timepoints for SIP and likely captures bacterial growth due to substrate addition more so than secondary feeding.

## Get bulk
bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
  select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
  mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))

evenness_early_meta.df = alpha_div_treat.df %>%
  filter(method == "evenness", time_point == "Early") %>%
  tidyr::spread(key=treatment, value=diversity) %>%
  mutate(delta_evenness = Treatment-Control) %>%
  left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare to zero
early_evenness_wilcox.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  model = wilcox.test(x = filter(evenness_early_meta.df, substrate==sub)$delta_evenness, alternative = "less", mu=0)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        Vstat = model$statistic,
                        pvalue = model$p.value)
  early_evenness_wilcox.df = rbind(early_evenness_wilcox.df, model.df)
}

early_evenness_wilcox.df = early_evenness_wilcox.df %>%
  mutate(padj = p.adjust(pvalue, method = "BH", n = 5)) %>%
  mutate(sig = ifelse(padj < 0.001, "***",
                      ifelse(padj < 0.01, "**",
                             ifelse(padj < 0.05, "*", "NS"))))

early_evenness_wilcox.plot = ggplot(data=evenness_early_meta.df, aes(x=substrate, y=delta_evenness)) +
  geom_hline(yintercept = 0, linetype=2, color="red") +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color=ecosystem), alpha=0.5) +
  geom_text(data=filter(early_evenness_wilcox.df, padj < 0.05), aes(x=substrate, label=sig), y=0.06, size=6) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  labs(x="Substrate", y="Change in evenness", color="Land-use") +
  lims(y=c(-0.3, 0.1)) +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=45, vjust=1, hjust=1),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top")


## Compare with organic content
early_SOM_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_early_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$organic_content_perc, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_SOM_evenness.df = rbind(early_SOM_evenness.df, model.df)
}

early_evenness_SOM.plot = ggplot(data=evenness_early_meta.df, aes(x=organic_content_perc, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_SOM_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.03, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=early_SOM_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.03, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="% SOM in bulk soil", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
early_DNA_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_early_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$DNA_conc__ng_ul, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_DNA_evenness.df = rbind(early_DNA_evenness.df, model.df)
}

early_evenness_DNA.plot = ggplot(data=evenness_early_meta.df, aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_DNA_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=5, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=early_DNA_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=5, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

early_landuse.leg = g_legend(early_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                             legend.box.background = element_rect(colour = "black")))

early_alpha_SOM.plot = cowplot::plot_grid(early_evenness_wilcox.plot + theme(legend.position = "none"),
                                          early_evenness_SOM.plot + theme(legend.position = "none", axis.title.y = element_blank()),
                                          early_landuse.leg,
                                          early_evenness_DNA.plot + theme(legend.position = "none", axis.title.y = element_blank()), 
                                          ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

early_alpha_SOM.plot

early_evenness_wilcox.df
##    substrate Vstat       pvalue         padj sig
## V    12C-Xyl   177 1.310611e-01 2.098336e-01  NS
## V1   12C-Ami    70 2.300534e-04 5.751336e-04 ***
## V2   12C-Van    15 1.275912e-07 6.379560e-07 ***
## V3   12C-Cel   210 3.277192e-01 3.277192e-01  NS
## V4   12C-Pal   172 1.678669e-01 2.098336e-01  NS
early_SOM_evenness.df
##      substrate         r      pvalue       padj
## cor    12C-Xyl 0.4377734 0.015545185 0.07772593
## cor1   12C-Ami 0.4648663 0.009646017 0.04823009
## cor2   12C-Van 0.3574692 0.052456283 0.26228141
## cor3   12C-Cel 0.3947730 0.030854962 0.15427481
## cor4   12C-Pal 0.1486803 0.441452927 1.00000000
early_DNA_evenness.df
##      substrate         r       pvalue        padj
## cor    12C-Xyl 0.4805024 7.197024e-03 0.035985118
## cor1   12C-Ami 0.6554694 8.442419e-05 0.000422121
## cor2   12C-Van 0.5498244 1.647087e-03 0.008235434
## cor3   12C-Cel 0.3027813 1.038712e-01 0.519355835
## cor4   12C-Pal 0.1253467 5.170601e-01 1.000000000

Late timepoints

Late timepoints are the second timepoint for each of the substrates (Day 4 for Xylose, amino acids, and vanillin; Day 28 for palmitic acid and cellulose). This is similar to the late timepoints for SIP and likely captures bacterial growth due more to secondary feeding and late feeding on substrates.

## Get bulk
bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
  select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
  mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))

evenness_late_meta.df = alpha_div_treat.df %>%
  filter(method == "evenness", time_point == "Late") %>%
  tidyr::spread(key=treatment, value=diversity) %>%
  mutate(delta_evenness = Treatment-Control) %>%
  left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare to zero
late_evenness_wilcox.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  model = wilcox.test(x = filter(evenness_late_meta.df, substrate==sub)$delta_evenness, alternative = "less", mu=0)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        Vstat = model$statistic,
                        pvalue = model$p.value)
  late_evenness_wilcox.df = rbind(late_evenness_wilcox.df, model.df)
}

late_evenness_wilcox.df = late_evenness_wilcox.df %>%
  mutate(padj = p.adjust(pvalue, method = "BH", n = 5)) %>%
  mutate(sig = ifelse(padj < 0.001, "***",
                      ifelse(padj < 0.01, "**",
                             ifelse(padj < 0.05, "*", "NS"))))

late_evenness_wilcox.plot = ggplot(data=evenness_late_meta.df, aes(x=substrate, y=delta_evenness)) +
  geom_hline(yintercept = 0, linetype=2, color="red") +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color=ecosystem), alpha=0.5) +
  geom_text(data=filter(late_evenness_wilcox.df, padj < 0.05), aes(x=substrate, label=sig), y=0.06, size=6) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  labs(x="Substrate", y="Change in evenness", color="Land-use") +
  lims(y=c(-0.3, 0.1)) +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=45, vjust=1, hjust=1),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top")


## Compare with organic content

late_SOM_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_late_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$organic_content_perc, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_SOM_evenness.df = rbind(late_SOM_evenness.df, model.df)
}

late_evenness_SOM.plot = ggplot(data=evenness_late_meta.df, aes(x=organic_content_perc, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_SOM_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.03, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=late_SOM_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.03, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="% SOM in bulk soil", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
late_DNA_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_late_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$DNA_conc__ng_ul, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_DNA_evenness.df = rbind(late_DNA_evenness.df, model.df)
}

late_evenness_DNA.plot = ggplot(data=evenness_late_meta.df, aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_DNA_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=3, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=late_DNA_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=3, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

late_landuse.leg = g_legend(late_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                           legend.box.background = element_rect(colour = "black")))

cowplot::plot_grid(late_evenness_wilcox.plot + theme(legend.position = "none"),
                   late_evenness_SOM.plot + theme(legend.position = "none", axis.title.y = element_blank()),
                   late_landuse.leg,
                   late_evenness_DNA.plot + theme(legend.position = "none", axis.title.y = element_blank()), 
                   ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

late_evenness_wilcox.df
##    substrate Vstat       pvalue         padj sig
## V    12C-Xyl   215 3.651710e-01 6.086183e-01  NS
## V1   12C-Ami    98 2.332402e-03 5.831006e-03  **
## V2   12C-Van    18 2.356246e-07 1.178123e-06 ***
## V3   12C-Cel   230 6.090689e-01 7.526750e-01  NS
## V4   12C-Pal   249 7.526750e-01 7.526750e-01  NS
late_SOM_evenness.df
##      substrate           r       pvalue        padj
## cor    12C-Xyl  0.05516903 0.7721575116 1.000000000
## cor1   12C-Ami  0.41074059 0.0241539493 0.120769746
## cor2   12C-Van  0.58126223 0.0007557696 0.003778848
## cor3   12C-Cel -0.33922920 0.0718198662 0.359099331
## cor4   12C-Pal -0.05088678 0.7932062614 1.000000000
late_DNA_evenness.df
##      substrate            r       pvalue        padj
## cor    12C-Xyl  0.026575752 0.8891335818 1.000000000
## cor1   12C-Ami  0.383383545 0.0364996598 0.182498299
## cor2   12C-Van  0.585679112 0.0006731203 0.003365602
## cor3   12C-Cel -0.194922309 0.3109276590 1.000000000
## cor4   12C-Pal  0.005407977 0.9777882112 1.000000000

Community composition difference between treatment and control microcosms

Since these are enrichments we cannot identify specific OTUs that have taken up a given substrate, however we can look at the community level change due to the substrate by comparing the treament microcosms to their corresponding water controls.

The first thing I need to do is measure the distance or dissimilarity between all microcosm communities. I will use three metrics for this: Bray-Curtis dissimilarity, unweighted UniFrac distance, and weighted UniFrac.

enr_BC.dist = vegdist(t(otu_table(enr.rare.physeq)), method="bray", binary=FALSE, diag=TRUE, upper=TRUE)
enr_uwUF.dist = distance(enr.rare.physeq, method="unifrac")
enr_wUF.dist = distance(enr.rare.physeq, method="wunifrac")

PERMANOVA

print("Bray-Curtis dissimilarity")
## [1] "Bray-Curtis dissimilarity"
enr_BC.adonis = adonis(formula = enr_BC.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_BC.adonis
## 
## Call:
## adonis(formula = enr_BC.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq),      "data.frame")) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                          Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## ecosystem                 2    11.411  5.7053 28.3341 0.12269  0.001 ***
## substrate                 5     2.397  0.4794  2.3807 0.02577  0.001 ***
## day                       1     0.357  0.3571  1.7734 0.00384  0.062 .  
## ecosystem:substrate      10     1.156  0.1156  0.5741 0.01243  1.000    
## ecosystem:day             2     0.185  0.0923  0.4583 0.00198  0.999    
## substrate:day             5     0.303  0.0606  0.3010 0.00326  1.000    
## ecosystem:substrate:day  10     0.475  0.0475  0.2361 0.00511  1.000    
## Residuals               381    76.718  0.2014         0.82491           
## Total                   416    93.002                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("-----")
## [1] "-----"
print("Unweighted UniFrac distance")
## [1] "Unweighted UniFrac distance"
enr_uwUF.adonis = adonis(formula = enr_uwUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_uwUF.adonis
## 
## Call:
## adonis(formula = enr_uwUF.dist ~ ecosystem * substrate * day,      data = as(sample_data(enr.rare.physeq), "data.frame")) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                          Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## ecosystem                 2     5.890 2.94479 15.8179 0.07255  0.001 ***
## substrate                 5     1.071 0.21426  1.1509 0.01320  0.131    
## day                       1     0.276 0.27590  1.4820 0.00340  0.068 .  
## ecosystem:substrate      10     1.155 0.11548  0.6203 0.01422  1.000    
## ecosystem:day             2     0.241 0.12073  0.6485 0.00297  0.998    
## substrate:day             5     0.575 0.11499  0.6177 0.00708  1.000    
## ecosystem:substrate:day  10     1.046 0.10459  0.5618 0.01288  1.000    
## Residuals               381    70.930 0.18617         0.87370           
## Total                   416    81.184                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("-----")
## [1] "-----"
print("Weighted UniFrac distance")
## [1] "Weighted UniFrac distance"
enr_wUF.adonis = adonis(formula = enr_wUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_wUF.adonis
## 
## Call:
## adonis(formula = enr_wUF.dist ~ ecosystem * substrate * day,      data = as(sample_data(enr.rare.physeq), "data.frame")) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                          Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## ecosystem                 2    1.9896 0.99480  33.384 0.13575  0.001 ***
## substrate                 5    0.8335 0.16670   5.594 0.05687  0.001 ***
## day                       1    0.1283 0.12832   4.306 0.00876  0.002 ** 
## ecosystem:substrate      10    0.1916 0.01916   0.643 0.01307  0.992    
## ecosystem:day             2    0.0312 0.01559   0.523 0.00213  0.930    
## substrate:day             5    0.0649 0.01298   0.435 0.00443  1.000    
## ecosystem:substrate:day  10    0.0638 0.00638   0.214 0.00436  1.000    
## Residuals               381   11.3532 0.02980         0.77464           
## Total                   416   14.6561                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("-----")
## [1] "-----"

Distances/Dissimilarities

Substrate significantly explains varaition in community composition as does ecosystem. Now I want to see if the differences between water controls and treatments correlate with either SOM or [DNA].

bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
  select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
  mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))

H2O.metadata = data.frame(sample_data(enr.rare.physeq)) %>%
  mutate(X.Sample = as.character(X.Sample)) %>%
  filter(substrate == "H2O-Con") %>%
  rename(Control = X.Sample) %>%
  select(Control, day, ecosystem, location)

treat.metadata = data.frame(sample_data(enr.rare.physeq)) %>%
  mutate(X.Sample = as.character(X.Sample)) %>%
  filter(substrate != "H2O-Con") %>%
  rename(Treatment = X.Sample) %>%
  select(Treatment, substrate, day, ecosystem, location)

paired.metadata = inner_join(H2O.metadata, treat.metadata, by = c("day", "ecosystem", "location"))

enr_BC.dist.df = as.matrix(enr_BC.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
  as.data.frame %>%
  tibble::rownames_to_column(var="X.Sample") %>%
  rename(Treatment = X.Sample) %>%
  tidyr::gather(key=Control, value=bray, -Treatment)

enr_uwUF.dist.df = as.matrix(enr_uwUF.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
  as.data.frame %>%
  tibble::rownames_to_column(var="X.Sample") %>%
  rename(Treatment = X.Sample) %>%
  tidyr::gather(key=Control, value=uwUF, -Treatment)

enr_wUF.dist.df = as.matrix(enr_wUF.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
  as.data.frame %>%
  tibble::rownames_to_column(var="X.Sample") %>%
  rename(Treatment = X.Sample) %>%
  tidyr::gather(key=Control, value=wUF, -Treatment)

enr_dist.df = full_join(enr_BC.dist.df, enr_uwUF.dist.df, by = c("Treatment", "Control")) %>%
  full_join(enr_wUF.dist.df, by = c("Treatment", "Control")) %>%
  inner_join(paired.metadata, by = c("Treatment", "Control")) %>%
  mutate(time_point = ifelse(day %in% c(2, 14), "Early", "Late"),
         ecosystem = factor(ecosystem, levels = c("agriculture", "meadow", "forest")),
         substrate = factor(substrate, levels = c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal"))) %>%
  left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
  mutate(location = gsub("_", " ", location))

Early timepoints

## Filter to just early timepoints
early_enr_dist.df = enr_dist.df %>%
  filter(time_point == "Early") %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare with organic content
early_SOM_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(early_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$organic_content_perc, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_SOM_wUF.df = rbind(early_SOM_wUF.df, model.df)
}

early_wUF_SOM.plot = ggplot(data=early_enr_dist.df, aes(x=organic_content_perc, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_SOM_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.04, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=early_SOM_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.04, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="% SOM in bulk soil", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
early_DNA_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(early_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$DNA_conc__ng_ul, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_DNA_wUF.df = rbind(early_DNA_wUF.df, model.df)
}

early_wUF_DNA.plot = ggplot(data=early_enr_dist.df, aes(x=DNA_conc__ng_ul, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_DNA_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=5, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=early_DNA_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=5, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

early_landuse.leg = g_legend(early_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                                        legend.position = "top",
                                                        legend.margin=unit(c(0,0,0,0),"cm")))

cowplot::plot_grid(early_landuse.leg,
                   early_wUF_SOM.plot + theme(legend.position = "none"),
                   early_wUF_DNA.plot + theme(legend.position = "none"), 
                   ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

early_SOM_wUF.df
##      substrate          r      pvalue        padj
## cor    12C-Xyl -0.4394740 0.015102735 0.075513675
## cor1   12C-Ami -0.5635586 0.001183091 0.005915455
## cor2   12C-Van -0.5085936 0.004106756 0.020533781
## cor3   12C-Cel -0.3298842 0.075028616 0.375143082
## cor4   12C-Pal -0.3912330 0.035847250 0.179236250
early_DNA_wUF.df
##      substrate          r       pvalue        padj
## cor    12C-Xyl -0.3919137 0.0322006083 0.161003042
## cor1   12C-Ami -0.5861507 0.0006647827 0.003323913
## cor2   12C-Van -0.5794285 0.0007926151 0.003963076
## cor3   12C-Cel -0.1307706 0.4909543483 1.000000000
## cor4   12C-Pal -0.3894321 0.0367842529 0.183921265

Late timepoints

## Filter to just late timepoints
late_enr_dist.df = enr_dist.df %>%
  filter(time_point == "Late") %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare with organic content
late_SOM_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(late_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$organic_content_perc, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_SOM_wUF.df = rbind(late_SOM_wUF.df, model.df)
}

late_wUF_SOM.plot = ggplot(data=late_enr_dist.df, aes(x=organic_content_perc, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_SOM_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.04, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=late_SOM_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.04, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="% SOM in bulk soil", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
late_DNA_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(late_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$DNA_conc__ng_ul, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_DNA_wUF.df = rbind(late_DNA_wUF.df, model.df)
}

late_wUF_DNA.plot = ggplot(data=late_enr_dist.df, aes(x=DNA_conc__ng_ul, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_DNA_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=5, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=late_DNA_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=5, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

late_landuse.leg = g_legend(late_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                            legend.position = "top",
                                            legend.margin=unit(c(0,0,0,0),"cm")))

cowplot::plot_grid(late_landuse.leg,
                   late_wUF_SOM.plot + theme(legend.position = "none"),
                   late_wUF_DNA.plot + theme(legend.position = "none"), 
                   ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

late_SOM_wUF.df
##      substrate          r       pvalue        padj
## cor    12C-Xyl -0.5665699 0.0010981916 0.005490958
## cor1   12C-Ami -0.4295573 0.0178374274 0.089187137
## cor2   12C-Van -0.6256795 0.0002176509 0.001088255
## cor3   12C-Cel -0.4483317 0.0147202467 0.073601234
## cor4   12C-Pal -0.4150801 0.0251522068 0.125761034
late_DNA_wUF.df
##      substrate          r       pvalue        padj
## cor    12C-Xyl -0.5763353 0.0008583402 0.004291701
## cor1   12C-Ami -0.2342091 0.2128667039 1.000000000
## cor2   12C-Van -0.5619261 0.0012314691 0.006157345
## cor3   12C-Cel -0.4051031 0.0292561374 0.146280687
## cor4   12C-Pal -0.2615567 0.1705101106 0.852550553

Diversity plots for publication

These will be the plots from the analyses above that are used in the publication of this study (and my PhD dissertation).

Early timepoints

early_landuse.leg = g_legend(early_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                             legend.box.background = element_rect(colour = "black")))

early_DNAyield.plot = cowplot::plot_grid(early_evenness_wilcox.plot + theme(legend.position = "none"),
                                         early_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
                                         early_landuse.leg,
                                         early_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"), 
                                         ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

early_DNAyield.plot

#ggsave(early_DNAyield.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/fig2_5.tiff", 
#       device = "tiff", width = 7, height = 6, units = "in")

For publication

carbon.conv = data.frame(substrate = c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal"),
                         carbon = factor(c("Xylose", "Amino acids", "Vanillin", "Cellulose", "Palmitic acid"),
                                         levels=c("Xylose", "Amino acids", "Vanillin", "Cellulose", "Palmitic acid")))

pub_early_evenness_wilcox.plot = ggplot(data=left_join(evenness_early_meta.df, carbon.conv, by="substrate"), aes(x=carbon, y=delta_evenness)) +
  geom_hline(yintercept = 0, linetype=2, color="red") +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color=ecosystem), alpha=0.5, size=1.5) +
  geom_text(data=filter(left_join(early_evenness_wilcox.df, carbon.conv, by="substrate"), padj < 0.05), aes(x=carbon, label=sig), y=0.06, size=4) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  labs(x="Substrate", y="Change in evenness", color="Land-use") +
  lims(y=c(-0.3, 0.1)) +
  theme_bw() + 
  theme(axis.text.x = element_text(size=6, angle=45, vjust=1, hjust=1),
        axis.text.y = element_text(size=6),
        axis.title = element_text(size=7),
        axis.ticks = element_line(size=0.2),
        strip.text = element_text(size=6),
        legend.text = element_text(size=6),
        legend.title = element_text(size=7),
        legend.position = "right",
        legend.box.background = element_rect(colour = "black"))

pub_early_evenness_DNA.plot = ggplot(data=left_join(evenness_early_meta.df, carbon.conv, by="substrate"), aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="black", size=0.75) +
  geom_point(aes(color=ecosystem), size=1.5) +
  geom_text(data=left_join(early_DNA_evenness.df, carbon.conv, by="substrate"), aes(label=paste("r==", round(r, 3), sep="")),
            x=15, y=-0.2, hjust=0, parse = TRUE, size=(6*5/14)) +
  geom_text(data=left_join(early_DNA_evenness.df, carbon.conv, by="substrate"), aes(label=paste("p==", round(padj, 3), sep="")),
            x=15, y=-0.235, hjust=0, parse = TRUE, size=(6*5/14)) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  scale_x_continuous(breaks=c(0,  50, 100)) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=6, angle=90, vjust=0.5),
        axis.text.y = element_text(size=6),
        axis.title = element_text(size=7),
        axis.ticks = element_line(size=0.2),
        strip.text = element_text(size=6),
        legend.text = element_text(size=6),
        legend.title = element_text(size=7),
        legend.position = "right") +
  facet_wrap(~carbon, nrow=1)


pub_early_wUF_DNA.plot = ggplot(data=left_join(early_enr_dist.df, carbon.conv, by="substrate"), aes(x=DNA_conc__ng_ul, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="black", size=0.75) +
  geom_point(aes(color=ecosystem), size=1.5) +
  geom_text(data=left_join(early_DNA_wUF.df, carbon.conv, by="substrate"), aes(label=paste("r==", round(r, 3), sep="")),
            x=15, y=0.4, hjust=0, parse = TRUE, size=(6*5/14)) +
  geom_text(data=left_join(early_DNA_wUF.df, carbon.conv, by="substrate"), aes(label=paste("p==", round(padj, 3), sep="")),
            x=15, y=0.36, hjust=0, parse = TRUE, size=(6*5/14)) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  scale_x_continuous(breaks=c(0,  50, 100)) +
  lims(y=c(0, 0.45)) +
  labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=6, angle=90, vjust=0.5),
        axis.text.y = element_text(size=6),
        axis.title = element_text(size=7),
        axis.ticks = element_line(size=0.2),
        strip.text = element_text(size=6),
        legend.text = element_text(size=6),
        legend.title = element_text(size=7),
        legend.position = "right") +
  facet_wrap(~carbon, nrow=1)

pub_early_landuse.leg = g_legend(pub_early_wUF_DNA.plot + theme(legend.box.background = element_rect(colour = "black"),
                                                                legend.title = element_text(size=7, hjust=0.5)))


pub_early_evenness_wilcox_leg.plot = cowplot::plot_grid(pub_early_evenness_wilcox.plot + theme(legend.position = "none"), 
                                                        pub_early_landuse.leg, ncol=2, rel_widths = c(1, 0.41))

pub_early_DNAyield.plot = cowplot::plot_grid(pub_early_evenness_wilcox_leg.plot,
                                             pub_early_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
                                             pub_early_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"), 
                                             ncol=1, labels=c("a", "b", "c"), label_size = 10, rel_heights = c(0.9, 1, 1))

pub_early_DNAyield.plot

#ggsave(pub_early_DNAyield.plot, filename = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/manuscript/Figures/Fig3.tiff", 
#       device = "tiff", width = 3.46457, height = 7.08661, units = "in")
early_landuse.leg = g_legend(early_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                                        legend.position = "top",
                                                        legend.margin=unit(c(0,0,0,0),"cm")))
early_SOM.plot = cowplot::plot_grid(early_landuse.leg,
                                    early_evenness_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Change in evenness"),
                                    early_wUF_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Weighted UniFrac distance"), 
                                    ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

early_SOM.plot

#ggsave(early_SOM.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_11.tiff", 
#       device = "tiff", width = 5, height = 7, units = "in")

Late timepoints

late_landuse.leg = g_legend(late_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                           legend.box.background = element_rect(colour = "black")))

late_DNAyield.plot = cowplot::plot_grid(late_evenness_wilcox.plot + theme(legend.position = "none"),
                                        late_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
                                        late_landuse.leg,
                                        late_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"), 
                                        ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

late_DNAyield.plot

#ggsave(late_DNAyield.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_12.tiff", 
#       device = "tiff", width = 7, height = 6, units = "in")
late_landuse.leg = g_legend(late_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                                      legend.position = "top",
                                                      legend.margin=unit(c(0,0,0,0),"cm")))
late_SOM.plot = cowplot::plot_grid(late_landuse.leg,
                                   late_evenness_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Change in evenness"),
                                   late_wUF_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Weighted UniFrac distance"), 
                                   ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

late_SOM.plot

#ggsave(late_SOM.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_13.tiff", 
#       device = "tiff", width = 5, height = 7, units = "in")

DESeq2 significant enrichments

Now I want to see if I can identify any OTUs that are significantly enriched in the substrate treated samples compared to the control samples.

library(DESeq2)
library(knitr)

enr.deseq.df = data.frame()
for (eco in c("agriculture", "meadow", "forest")){
  for (carbon in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
    for (dia in unique(filter(data.frame(sample_data(enr.physeq)), ecosystem == eco, substrate == carbon)$day)){
      sub.physeq = subset_samples(enr.physeq, ecosystem == eco & substrate %in% c(carbon, "H2O-Con") & day == dia)
      sub.physeq = prune_taxa(taxa_sums(sub.physeq) > 0, sub.physeq)
      sample_data(sub.physeq)$substrate = as.factor(sample_data(sub.physeq)$substrate)
      sample_data(sub.physeq)$substrate = relevel(sample_data(sub.physeq)$substrate, "H2O-Con")
      
      OTU.table = as.matrix(otu_table(sub.physeq))
      OTU.table[OTU.table < 5] = 0
      OTU.table[OTU.table >= 5] = 1
      sparse = length(sample_names(sub.physeq)) * 0.25
      sampleCount.df = data.frame(sample = rowSums(OTU.table)) %>%
        tibble::rownames_to_column(var="OTU") %>%
        filter(sample >= sparse)
      sub.physeq = prune_taxa(sampleCount.df$OTU, sub.physeq)
      sub.deseq = phyloseq_to_deseq2(sub.physeq, ~ location + substrate)
      sub.deseq = DESeq(sub.deseq, betaPrior=TRUE)
      sub.deseq.res = results(sub.deseq, lfcThreshold = .25,
                              contrast = c("substrate", carbon, "H2O-Con"), 
                              altHypothesis = "greater",
                              test="Wald")
      enr.deseq.df = rbind(enr.deseq.df, data.frame(sub.deseq.res) %>%
                             tibble::rownames_to_column(var="OTU") %>%
                             mutate(substrate = carbon, ecosystem = eco, day = dia))
    }
  }
}

#write.table(enr.deseq.df, file = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/enrichment_DESeq2_l2fc.txt", sep="\t", quote=FALSE, row.names = FALSE)
kable(enr.deseq.df %>% 
        filter(padj < 0.05) %>%
        group_by(substrate, ecosystem, day) %>%
        mutate(n_total_OTU = n()))
OTU baseMean log2FoldChange lfcSE stat pvalue padj substrate ecosystem day n_total_OTU
OTU.463 57.143056 1.7055406 0.3470587 4.193932 0.0000137 0.0118094 12C-Xyl agriculture 4 3
OTU.66 108.855465 1.8827157 0.2496612 6.539725 0.0000000 0.0000001 12C-Xyl agriculture 4 3
OTU.2 215.880615 1.1472726 0.2351432 3.815856 0.0000679 0.0389719 12C-Xyl agriculture 4 3
OTU.9 823.875478 1.4954986 0.1899400 6.557326 0.0000000 0.0000000 12C-Xyl agriculture 2 10
OTU.463 176.220818 2.3651105 0.3061382 6.909005 0.0000000 0.0000000 12C-Xyl agriculture 2 10
OTU.1541 63.550455 1.1522105 0.2627946 3.433139 0.0002983 0.0098445 12C-Xyl agriculture 2 10
OTU.951 84.136147 0.8679262 0.2059635 3.000173 0.0013491 0.0400692 12C-Xyl agriculture 2 10
OTU.154 83.888332 1.5065962 0.2482874 5.061054 0.0000002 0.0000155 12C-Xyl agriculture 2 10
OTU.315 477.770963 1.5345565 0.3461871 3.710585 0.0001034 0.0051178 12C-Xyl agriculture 2 10
OTU.8 1616.901682 1.2408664 0.2885439 3.434023 0.0002973 0.0098445 12C-Xyl agriculture 2 10
OTU.66 124.839100 1.9549236 0.3147849 5.416154 0.0000000 0.0000030 12C-Xyl agriculture 2 10
OTU.2 249.987495 1.3658821 0.3072398 3.631958 0.0001406 0.0059671 12C-Xyl agriculture 2 10
OTU.187 64.905291 1.6165815 0.2975099 4.593398 0.0000022 0.0001295 12C-Xyl agriculture 2 10
OTU.9 1402.221020 2.2974544 0.2917992 7.016655 0.0000000 0.0000000 12C-Ami agriculture 4 14
OTU.463 148.253559 2.4180415 0.3429677 6.321416 0.0000000 0.0000000 12C-Ami agriculture 4 14
OTU.3782 12.433210 1.9041180 0.3626178 4.561601 0.0000025 0.0002191 12C-Ami agriculture 4 14
OTU.315 392.139985 1.9600863 0.2492455 6.861052 0.0000000 0.0000000 12C-Ami agriculture 4 14
OTU.8 1100.658842 1.6892232 0.2190543 6.570168 0.0000000 0.0000000 12C-Ami agriculture 4 14
OTU.2396 27.086295 1.8923076 0.3811749 4.308541 0.0000082 0.0006384 12C-Ami agriculture 4 14
OTU.23 93.608292 1.5439977 0.2326064 5.563036 0.0000000 0.0000015 12C-Ami agriculture 4 14
OTU.1386 58.258922 1.2905180 0.2966817 3.507186 0.0002264 0.0125672 12C-Ami agriculture 4 14
OTU.4461 24.743522 1.5908981 0.3615954 3.708283 0.0001043 0.0067557 12C-Ami agriculture 4 14
OTU.7776 115.221961 0.8563077 0.1714353 3.536657 0.0002026 0.0121100 12C-Ami agriculture 4 14
OTU.66 179.139721 2.6579874 0.2494062 9.654884 0.0000000 0.0000000 12C-Ami agriculture 4 14
OTU.2 460.160815 1.6191671 0.3311080 4.135108 0.0000177 0.0012530 12C-Ami agriculture 4 14
OTU.16149 21.593242 3.4617346 0.4060604 7.909500 0.0000000 0.0000000 12C-Ami agriculture 4 14
OTU.3 31.418578 2.3544258 0.4180531 5.033872 0.0000002 0.0000233 12C-Ami agriculture 4 14
OTU.3589 14.800817 1.5392306 0.4049181 3.183929 0.0007265 0.0303748 12C-Ami agriculture 2 17
OTU.9 2031.842266 2.6071517 0.2382931 9.891816 0.0000000 0.0000000 12C-Ami agriculture 2 17
OTU.463 245.424035 2.6656187 0.3248678 7.435699 0.0000000 0.0000000 12C-Ami agriculture 2 17
OTU.3782 14.552128 2.0661450 0.3948885 4.599134 0.0000021 0.0001774 12C-Ami agriculture 2 17
OTU.315 535.845657 2.2230735 0.2927950 6.738753 0.0000000 0.0000000 12C-Ami agriculture 2 17
OTU.8 1912.137734 1.8269501 0.2647118 5.957233 0.0000000 0.0000001 12C-Ami agriculture 2 17
OTU.2396 43.956143 1.8111898 0.4107461 3.800863 0.0000721 0.0043848 12C-Ami agriculture 2 17
OTU.2513 62.213661 1.1788555 0.2616336 3.550216 0.0001925 0.0091967 12C-Ami agriculture 2 17
OTU.1386 66.543042 2.1527448 0.2855800 6.662739 0.0000000 0.0000000 12C-Ami agriculture 2 17
OTU.4461 32.142982 1.6209049 0.3337316 4.107807 0.0000200 0.0013361 12C-Ami agriculture 2 17
OTU.193 72.464175 2.3130630 0.4535396 4.548804 0.0000027 0.0002005 12C-Ami agriculture 2 17
OTU.20 757.867525 0.7834658 0.1667190 3.199790 0.0006876 0.0303748 12C-Ami agriculture 2 17
OTU.66 210.384740 2.3425108 0.3797888 5.509670 0.0000000 0.0000017 12C-Ami agriculture 2 17
OTU.2 546.506627 1.6335341 0.3685922 3.753563 0.0000872 0.0048597 12C-Ami agriculture 2 17
OTU.3 113.241648 2.8100466 0.4301395 5.951666 0.0000000 0.0000001 12C-Ami agriculture 2 17
OTU.711 32.047650 1.3359246 0.3043876 3.567572 0.0001802 0.0091967 12C-Ami agriculture 2 17
OTU.187 53.188007 1.0761760 0.2623858 3.148707 0.0008200 0.0322683 12C-Ami agriculture 2 17
OTU.1 980.734255 2.6471368 0.5128590 4.674066 0.0000015 0.0013451 12C-Van agriculture 2 4
OTU.66 211.157488 2.0560193 0.4797711 3.764335 0.0000835 0.0380326 12C-Van agriculture 2 4
OTU.2 2180.028131 2.1732379 0.4791039 4.014240 0.0000298 0.0181100 12C-Van agriculture 2 4
OTU.3 1596.345820 3.3653851 0.4892176 6.368097 0.0000000 0.0000002 12C-Van agriculture 2 4
OTU.8 1850.903401 2.3176915 0.3966413 5.213001 0.0000001 0.0000388 12C-Van agriculture 4 6
OTU.2396 90.659803 3.0985649 0.4914833 5.795852 0.0000000 0.0000028 12C-Van agriculture 4 6
OTU.1 589.750952 3.0252483 0.5020376 5.527970 0.0000000 0.0000090 12C-Van agriculture 4 6
OTU.66 167.914591 2.7058574 0.3884788 6.321728 0.0000000 0.0000002 12C-Van agriculture 4 6
OTU.2 1069.948292 2.3885562 0.4823726 4.433412 0.0000046 0.0012908 12C-Van agriculture 4 6
OTU.56 193.226833 2.0932142 0.3779803 4.876482 0.0000005 0.0001804 12C-Van agriculture 4 6
OTU.18 83.450918 1.4174655 0.2508977 4.653154 0.0000016 0.0032068 12C-Cel agriculture 14 1
OTU.131 43.772174 1.5132675 0.3099683 4.075473 0.0000230 0.0165366 12C-Pal agriculture 28 3
OTU.18 42.941360 1.4081744 0.2860221 4.049247 0.0000257 0.0165366 12C-Pal agriculture 28 3
OTU.4420 113.862921 2.3114293 0.3187109 6.468023 0.0000000 0.0000001 12C-Pal agriculture 28 3
OTU.9 307.578454 1.5465536 0.2465028 5.259791 0.0000001 0.0000171 12C-Xyl meadow 2 7
OTU.951 23.722085 1.4350722 0.2565998 4.618367 0.0000019 0.0003055 12C-Xyl meadow 2 7
OTU.154 110.673703 1.4643399 0.2625814 4.624622 0.0000019 0.0003055 12C-Xyl meadow 2 7
OTU.2396 19.764453 1.6472424 0.3547402 3.938777 0.0000409 0.0055457 12C-Xyl meadow 2 7
OTU.66 28.053223 2.0078524 0.2912671 6.035190 0.0000000 0.0000003 12C-Xyl meadow 2 7
OTU.2 180.311952 1.7821720 0.2239602 6.841270 0.0000000 0.0000000 12C-Xyl meadow 2 7
OTU.187 51.968729 1.6904341 0.2269908 6.345781 0.0000000 0.0000001 12C-Xyl meadow 2 7
OTU.2 291.607731 1.5134239 0.2343700 5.390724 0.0000000 0.0000269 12C-Ami meadow 4 2
OTU.3 67.375845 2.3313231 0.3385215 6.148274 0.0000000 0.0000006 12C-Ami meadow 4 2
OTU.9 390.716931 1.9120133 0.2430766 6.837407 0.0000000 0.0000000 12C-Ami meadow 2 8
OTU.3782 6.986803 1.5342235 0.3496703 3.672670 0.0001200 0.0213026 12C-Ami meadow 2 8
OTU.1863 63.905406 1.7674692 0.3457300 4.389175 0.0000057 0.0013464 12C-Ami meadow 2 8
OTU.315 168.680186 1.4360614 0.2688758 4.411186 0.0000051 0.0013464 12C-Ami meadow 2 8
OTU.66 27.246631 1.6959146 0.3149271 4.591267 0.0000022 0.0007820 12C-Ami meadow 2 8
OTU.2 256.841475 1.8312129 0.2188198 7.226096 0.0000000 0.0000000 12C-Ami meadow 2 8
OTU.16149 9.175487 1.7383960 0.3551714 4.190641 0.0000139 0.0028214 12C-Ami meadow 2 8
OTU.3 41.985175 2.6785968 0.3498060 6.942696 0.0000000 0.0000000 12C-Ami meadow 2 8
OTU.3782 18.765526 2.3314754 0.3499449 5.948010 0.0000000 0.0000010 12C-Van meadow 4 5
OTU.66 45.834871 1.7014041 0.3599458 4.032285 0.0000276 0.0104398 12C-Van meadow 4 5
OTU.2 828.031945 2.7981725 0.2536109 10.047569 0.0000000 0.0000000 12C-Van meadow 4 5
OTU.3 367.006897 1.8751572 0.3784055 4.294750 0.0000087 0.0044072 12C-Van meadow 4 5
OTU.29 23.315455 1.6340944 0.3734184 3.706551 0.0001051 0.0317673 12C-Van meadow 4 5
OTU.85 50.025534 1.4205890 0.3140419 3.727494 0.0000967 0.0070973 12C-Van meadow 2 12
OTU.35 83.736607 1.1173289 0.2567176 3.378533 0.0003644 0.0202832 12C-Van meadow 2 12
OTU.2977 43.992439 1.4438190 0.3223333 3.703679 0.0001062 0.0070973 12C-Van meadow 2 12
OTU.98 26.822922 1.5949465 0.3292538 4.084832 0.0000221 0.0019933 12C-Van meadow 2 12
OTU.3782 20.076912 3.0564123 0.3232447 8.682006 0.0000000 0.0000000 12C-Van meadow 2 12
OTU.1166 16.420254 1.6638561 0.2746476 5.147891 0.0000001 0.0000147 12C-Van meadow 2 12
OTU.2396 50.033273 2.6333978 0.3383678 7.043808 0.0000000 0.0000000 12C-Van meadow 2 12
OTU.66 46.853527 2.5619807 0.3194617 7.237114 0.0000000 0.0000000 12C-Van meadow 2 12
OTU.2 818.088037 2.2775018 0.3727713 5.438996 0.0000000 0.0000036 12C-Van meadow 2 12
OTU.16149 12.255583 1.6074319 0.4011831 3.383572 0.0003577 0.0202832 12C-Van meadow 2 12
OTU.3 598.496711 3.2789701 0.4011677 7.550383 0.0000000 0.0000000 12C-Van meadow 2 12
OTU.223 42.460611 1.9160213 0.4097038 4.066405 0.0000239 0.0019933 12C-Van meadow 2 12
OTU.1 354.827089 0.9391310 0.1456613 4.731050 0.0000011 0.0016920 12C-Xyl forest 4 2
OTU.2 2283.299024 1.2753339 0.2374383 4.318317 0.0000079 0.0059549 12C-Xyl forest 4 2
OTU.9 279.245634 1.2881788 0.1957360 5.303976 0.0000001 0.0000441 12C-Ami forest 2 2
OTU.3 161.998275 2.7876475 0.3552902 7.142463 0.0000000 0.0000000 12C-Ami forest 2 2
OTU.2 2911.058579 1.0187588 0.1898262 4.049804 0.0000256 0.0408034 12C-Ami forest 4 1
OTU.66 64.385275 1.5741686 0.2865746 4.620677 0.0000019 0.0026750 12C-Van forest 2 2
OTU.2 3957.520597 1.7175488 0.3265153 4.494579 0.0000035 0.0026750 12C-Van forest 2 2
OTU.2 4835.350981 1.4233702 0.2440426 4.808054 0.0000008 0.0012177 12C-Van forest 4 1

Taxonomy of enriched OTUs

enr.deseq.tax.df = data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE) %>%
  tibble::rownames_to_column(var="OTU") %>%
  right_join(enr.deseq.df, by="OTU")

enr.deseq.tax.sum = enr.deseq.tax.df %>%
  filter(padj < 0.05) %>%
  mutate(taxa = ifelse(Phylum == "Proteobacteria", Class, 
                       ifelse(is.na(Phylum), "Unclassified", Phylum)),
         period = ifelse(day %in% c(2, 14), "Early", "Late")) %>%
  group_by(ecosystem, substrate, period, taxa) %>%
  summarize(n_OTU = n()) %>%
  as.data.frame

ggplot(data = enr.deseq.tax.sum, aes(x=ecosystem, y=n_OTU, fill=taxa)) +
  geom_bar(stat = "identity") +
  facet_grid(substrate~period)

OTUs confirming SIP results

Are any of these substrate enriched OTUs also 13C-labeled in the SIP study?

## Save results for publication table
enr.tax.df = data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE) %>%
  tibble::rownames_to_column(var="OTU") %>%
  filter(OTU %in% filter(enr.deseq.df, padj < 0.05)$OTU)

incorp.enr.df = l2fc.df %>%
  filter(padj < 0.05) %>%
  select(OTU, substrate, ecosystem) %>%
  unique %>%
  mutate(substrate = gsub("13C", "12C", substrate),
         SIP_labeled = "Yes") %>%
  inner_join(enr.deseq.df %>% filter(padj < 0.05), 
             by = c("OTU", "substrate", "ecosystem")) %>%
  select(OTU, substrate, ecosystem, SIP_labeled) %>%
  right_join(enr.deseq.df %>% filter(padj < 0.05),
             by = c("OTU", "substrate", "ecosystem")) %>%
  mutate(SIP_labeled = ifelse(is.na(SIP_labeled), "No", SIP_labeled)) %>%
  left_join(enr.tax.df, by="OTU")

#write.table(incorp.enr.df, file = "/Users/sambarnett/Desktop/labeled_enrichment_DESeq2_l2fc.txt", sep="\t", quote=FALSE, row.names = FALSE)

kable(incorp.enr.df)
OTU substrate ecosystem SIP_labeled baseMean log2FoldChange lfcSE stat pvalue padj day Domain Phylum Class Order Family Genus Species
OTU.66 12C-Ami meadow Yes 27.246631 1.6959146 0.3149271 4.591267 0.0000022 0.0007820 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.9 12C-Ami meadow Yes 390.716931 1.9120133 0.2430766 6.837407 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.3 12C-Ami meadow Yes 67.375845 2.3313231 0.3385215 6.148274 0.0000000 0.0000006 4 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3 12C-Ami meadow Yes 41.985175 2.6785968 0.3498060 6.942696 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3 12C-Ami meadow Yes 67.375845 2.3313231 0.3385215 6.148274 0.0000000 0.0000006 4 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3 12C-Ami meadow Yes 41.985175 2.6785968 0.3498060 6.942696 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.2 12C-Ami meadow Yes 291.607731 1.5134239 0.2343700 5.390724 0.0000000 0.0000269 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Ami meadow Yes 256.841475 1.8312129 0.2188198 7.226096 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Ami meadow Yes 291.607731 1.5134239 0.2343700 5.390724 0.0000000 0.0000269 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Ami meadow Yes 256.841475 1.8312129 0.2188198 7.226096 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.3782 12C-Ami meadow Yes 6.986803 1.5342235 0.3496703 3.672670 0.0001200 0.0213026 2 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.16149 12C-Ami meadow Yes 9.175487 1.7383960 0.3551714 4.190641 0.0000139 0.0028214 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Pseudomonas koreensis
OTU.2 12C-Xyl agriculture Yes 215.880615 1.1472726 0.2351432 3.815856 0.0000679 0.0389719 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Xyl agriculture Yes 249.987495 1.3658821 0.3072398 3.631958 0.0001406 0.0059671 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Xyl agriculture Yes 215.880615 1.1472726 0.2351432 3.815856 0.0000679 0.0389719 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Xyl agriculture Yes 249.987495 1.3658821 0.3072398 3.631958 0.0001406 0.0059671 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Xyl agriculture Yes 108.855465 1.8827157 0.2496612 6.539725 0.0000000 0.0000001 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Xyl agriculture Yes 124.839100 1.9549236 0.3147849 5.416154 0.0000000 0.0000030 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Xyl agriculture Yes 108.855465 1.8827157 0.2496612 6.539725 0.0000000 0.0000001 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Xyl agriculture Yes 124.839100 1.9549236 0.3147849 5.416154 0.0000000 0.0000030 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.3 12C-Van meadow Yes 367.006897 1.8751572 0.3784055 4.294750 0.0000087 0.0044072 4 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3 12C-Van meadow Yes 598.496711 3.2789701 0.4011677 7.550383 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3 12C-Van meadow Yes 367.006897 1.8751572 0.3784055 4.294750 0.0000087 0.0044072 4 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3 12C-Van meadow Yes 598.496711 3.2789701 0.4011677 7.550383 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3782 12C-Van meadow Yes 18.765526 2.3314754 0.3499449 5.948010 0.0000000 0.0000010 4 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.3782 12C-Van meadow Yes 20.076912 3.0564123 0.3232447 8.682006 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.3782 12C-Van meadow Yes 18.765526 2.3314754 0.3499449 5.948010 0.0000000 0.0000010 4 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.3782 12C-Van meadow Yes 20.076912 3.0564123 0.3232447 8.682006 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.1166 12C-Van meadow Yes 16.420254 1.6638561 0.2746476 5.147891 0.0000001 0.0000147 2 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus Ambiguous_taxa
OTU.2 12C-Ami agriculture Yes 460.160815 1.6191671 0.3311080 4.135108 0.0000177 0.0012530 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Ami agriculture Yes 546.506627 1.6335341 0.3685922 3.753563 0.0000872 0.0048597 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Ami agriculture Yes 460.160815 1.6191671 0.3311080 4.135108 0.0000177 0.0012530 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Ami agriculture Yes 546.506627 1.6335341 0.3685922 3.753563 0.0000872 0.0048597 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Ami agriculture Yes 179.139721 2.6579874 0.2494062 9.654884 0.0000000 0.0000000 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Ami agriculture Yes 210.384740 2.3425108 0.3797888 5.509670 0.0000000 0.0000017 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Ami agriculture Yes 179.139721 2.6579874 0.2494062 9.654884 0.0000000 0.0000000 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Ami agriculture Yes 210.384740 2.3425108 0.3797888 5.509670 0.0000000 0.0000017 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.193 12C-Ami agriculture Yes 72.464175 2.3130630 0.4535396 4.548804 0.0000027 0.0002005 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Cupriavidus Ambiguous_taxa
OTU.16149 12C-Ami agriculture Yes 21.593242 3.4617346 0.4060604 7.909500 0.0000000 0.0000000 4 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Pseudomonas koreensis
OTU.3782 12C-Ami agriculture Yes 12.433210 1.9041180 0.3626178 4.561601 0.0000025 0.0002191 4 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.3782 12C-Ami agriculture Yes 14.552128 2.0661450 0.3948885 4.599134 0.0000021 0.0001774 2 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.3782 12C-Ami agriculture Yes 12.433210 1.9041180 0.3626178 4.561601 0.0000025 0.0002191 4 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.3782 12C-Ami agriculture Yes 14.552128 2.0661450 0.3948885 4.599134 0.0000021 0.0001774 2 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Rhodococcus NA
OTU.223 12C-Van meadow Yes 42.460611 1.9160213 0.4097038 4.066405 0.0000239 0.0019933 2 Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium uncultured bacterium
OTU.3 12C-Ami forest Yes 161.998275 2.7876475 0.3552902 7.142463 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.2 12C-Ami forest Yes 2911.058579 1.0187588 0.1898262 4.049804 0.0000256 0.0408034 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.9 12C-Ami agriculture Yes 1402.221020 2.2974544 0.2917992 7.016655 0.0000000 0.0000000 4 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.9 12C-Ami agriculture Yes 2031.842266 2.6071517 0.2382931 9.891816 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.9 12C-Ami agriculture Yes 1402.221020 2.2974544 0.2917992 7.016655 0.0000000 0.0000000 4 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.9 12C-Ami agriculture Yes 2031.842266 2.6071517 0.2382931 9.891816 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.315 12C-Ami agriculture Yes 392.139985 1.9600863 0.2492455 6.861052 0.0000000 0.0000000 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia uncultured bacterium
OTU.315 12C-Ami agriculture Yes 535.845657 2.2230735 0.2927950 6.738753 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia uncultured bacterium
OTU.315 12C-Ami agriculture Yes 392.139985 1.9600863 0.2492455 6.861052 0.0000000 0.0000000 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia uncultured bacterium
OTU.315 12C-Ami agriculture Yes 535.845657 2.2230735 0.2927950 6.738753 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia uncultured bacterium
OTU.23 12C-Ami agriculture Yes 93.608292 1.5439977 0.2326064 5.563036 0.0000000 0.0000015 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Ralstonia NA
OTU.20 12C-Ami agriculture Yes 757.867525 0.7834658 0.1667190 3.199790 0.0006876 0.0303748 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Ambiguous_taxa Ambiguous_taxa
OTU.463 12C-Ami agriculture Yes 148.253559 2.4180415 0.3429677 6.321416 0.0000000 0.0000000 4 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.463 12C-Ami agriculture Yes 245.424035 2.6656187 0.3248678 7.435699 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.463 12C-Ami agriculture Yes 148.253559 2.4180415 0.3429677 6.321416 0.0000000 0.0000000 4 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.463 12C-Ami agriculture Yes 245.424035 2.6656187 0.3248678 7.435699 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.1386 12C-Ami agriculture Yes 58.258922 1.2905180 0.2966817 3.507186 0.0002264 0.0125672 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.1386 12C-Ami agriculture Yes 66.543042 2.1527448 0.2855800 6.662739 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.1386 12C-Ami agriculture Yes 58.258922 1.2905180 0.2966817 3.507186 0.0002264 0.0125672 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.1386 12C-Ami agriculture Yes 66.543042 2.1527448 0.2855800 6.662739 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.2 12C-Xyl forest Yes 2283.299024 1.2753339 0.2374383 4.318317 0.0000079 0.0059549 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.1 12C-Xyl forest Yes 354.827089 0.9391310 0.1456613 4.731050 0.0000011 0.0016920 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia Ambiguous_taxa
OTU.66 12C-Van forest Yes 64.385275 1.5741686 0.2865746 4.620677 0.0000019 0.0026750 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van forest Yes 3957.520597 1.7175488 0.3265153 4.494579 0.0000035 0.0026750 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van forest Yes 4835.350981 1.4233702 0.2440426 4.808054 0.0000008 0.0012177 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van forest Yes 3957.520597 1.7175488 0.3265153 4.494579 0.0000035 0.0026750 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van forest Yes 4835.350981 1.4233702 0.2440426 4.808054 0.0000008 0.0012177 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.951 12C-Xyl meadow Yes 23.722085 1.4350722 0.2565998 4.618367 0.0000019 0.0003055 2 Bacteria Actinobacteria Actinobacteria Micrococcales Microbacteriaceae NA NA
OTU.9 12C-Xyl meadow Yes 307.578454 1.5465536 0.2465028 5.259791 0.0000001 0.0000171 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.154 12C-Xyl meadow Yes 110.673703 1.4643399 0.2625814 4.624622 0.0000019 0.0003055 2 Bacteria Actinobacteria Actinobacteria Micrococcales Cellulomonadaceae Cellulomonas Ambiguous_taxa
OTU.2 12C-Xyl meadow Yes 180.311952 1.7821720 0.2239602 6.841270 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Xyl meadow Yes 28.053223 2.0078524 0.2912671 6.035190 0.0000000 0.0000003 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2396 12C-Xyl meadow Yes 19.764453 1.6472424 0.3547402 3.938777 0.0000409 0.0055457 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.187 12C-Xyl meadow Yes 51.968729 1.6904341 0.2269908 6.345781 0.0000000 0.0000001 2 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium uncultured bacterium
OTU.2396 12C-Ami agriculture Yes 27.086295 1.8923076 0.3811749 4.308541 0.0000082 0.0006384 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.2396 12C-Ami agriculture Yes 43.956143 1.8111898 0.4107461 3.800863 0.0000721 0.0043848 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.2396 12C-Ami agriculture Yes 27.086295 1.8923076 0.3811749 4.308541 0.0000082 0.0006384 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.2396 12C-Ami agriculture Yes 43.956143 1.8111898 0.4107461 3.800863 0.0000721 0.0043848 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.8 12C-Van agriculture Yes 1850.903401 2.3176915 0.3966413 5.213001 0.0000001 0.0000388 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia Ambiguous_taxa
OTU.2396 12C-Van agriculture Yes 90.659803 3.0985649 0.4914833 5.795852 0.0000000 0.0000028 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.2 12C-Van agriculture Yes 2180.028131 2.1732379 0.4791039 4.014240 0.0000298 0.0181100 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van agriculture Yes 1069.948292 2.3885562 0.4823726 4.433412 0.0000046 0.0012908 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van agriculture Yes 2180.028131 2.1732379 0.4791039 4.014240 0.0000298 0.0181100 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van agriculture Yes 1069.948292 2.3885562 0.4823726 4.433412 0.0000046 0.0012908 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.1 12C-Van agriculture Yes 980.734255 2.6471368 0.5128590 4.674066 0.0000015 0.0013451 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia Ambiguous_taxa
OTU.1 12C-Van agriculture Yes 589.750952 3.0252483 0.5020376 5.527970 0.0000000 0.0000090 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia Ambiguous_taxa
OTU.1 12C-Van agriculture Yes 980.734255 2.6471368 0.5128590 4.674066 0.0000015 0.0013451 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia Ambiguous_taxa
OTU.1 12C-Van agriculture Yes 589.750952 3.0252483 0.5020376 5.527970 0.0000000 0.0000090 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia Ambiguous_taxa
OTU.8 12C-Xyl agriculture Yes 1616.901682 1.2408664 0.2885439 3.434023 0.0002973 0.0098445 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia Ambiguous_taxa
OTU.131 12C-Pal agriculture Yes 43.772174 1.5132675 0.3099683 4.075473 0.0000230 0.0165366 28 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Nocardia NA
OTU.56 12C-Van agriculture Yes 193.226833 2.0932142 0.3779803 4.876482 0.0000005 0.0001804 4 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Beijerinckiaceae Methylobacterium uncultured bacterium
OTU.1541 12C-Xyl agriculture Yes 63.550455 1.1522105 0.2627946 3.433139 0.0002983 0.0098445 2 Bacteria Actinobacteria Actinobacteria Micrococcales Microbacteriaceae Microbacterium Ambiguous_taxa
OTU.9 12C-Xyl agriculture Yes 823.875478 1.4954986 0.1899400 6.557326 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.18 12C-Cel agriculture Yes 83.450918 1.4174655 0.2508977 4.653154 0.0000016 0.0032068 14 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Duganella NA
OTU.3 12C-Van agriculture Yes 1596.345820 3.3653851 0.4892176 6.368097 0.0000000 0.0000002 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.463 12C-Xyl agriculture No 57.143056 1.7055406 0.3470587 4.193932 0.0000137 0.0118094 4 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.463 12C-Xyl agriculture No 176.220818 2.3651105 0.3061382 6.909005 0.0000000 0.0000000 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa
OTU.951 12C-Xyl agriculture No 84.136147 0.8679262 0.2059635 3.000173 0.0013491 0.0400692 2 Bacteria Actinobacteria Actinobacteria Micrococcales Microbacteriaceae NA NA
OTU.154 12C-Xyl agriculture No 83.888332 1.5065962 0.2482874 5.061054 0.0000002 0.0000155 2 Bacteria Actinobacteria Actinobacteria Micrococcales Cellulomonadaceae Cellulomonas Ambiguous_taxa
OTU.315 12C-Xyl agriculture No 477.770963 1.5345565 0.3461871 3.710585 0.0001034 0.0051178 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia uncultured bacterium
OTU.187 12C-Xyl agriculture No 64.905291 1.6165815 0.2975099 4.593398 0.0000022 0.0001295 2 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium uncultured bacterium
OTU.8 12C-Ami agriculture No 1100.658842 1.6892232 0.2190543 6.570168 0.0000000 0.0000000 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia Ambiguous_taxa
OTU.4461 12C-Ami agriculture No 24.743522 1.5908981 0.3615954 3.708283 0.0001043 0.0067557 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.7776 12C-Ami agriculture No 115.221961 0.8563077 0.1714353 3.536657 0.0002026 0.0121100 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.3 12C-Ami agriculture No 31.418578 2.3544258 0.4180531 5.033872 0.0000002 0.0000233 4 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.3589 12C-Ami agriculture No 14.800817 1.5392306 0.4049181 3.183929 0.0007265 0.0303748 2 Bacteria Bacteroidetes Bacteroidia Chitinophagales Chitinophagaceae Flavisolibacter NA
OTU.8 12C-Ami agriculture No 1912.137734 1.8269501 0.2647118 5.957233 0.0000000 0.0000001 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia Ambiguous_taxa
OTU.2513 12C-Ami agriculture No 62.213661 1.1788555 0.2616336 3.550216 0.0001925 0.0091967 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.4461 12C-Ami agriculture No 32.142982 1.6209049 0.3337316 4.107807 0.0000200 0.0013361 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA NA
OTU.3 12C-Ami agriculture No 113.241648 2.8100466 0.4301395 5.951666 0.0000000 0.0000001 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Ambiguous_taxa
OTU.711 12C-Ami agriculture No 32.047650 1.3359246 0.3043876 3.567572 0.0001802 0.0091967 2 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Beijerinckiaceae Bosea uncultured bacterium
OTU.187 12C-Ami agriculture No 53.188007 1.0761760 0.2623858 3.148707 0.0008200 0.0322683 2 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium uncultured bacterium
OTU.66 12C-Van agriculture No 211.157488 2.0560193 0.4797711 3.764335 0.0000835 0.0380326 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.66 12C-Van agriculture No 167.914591 2.7058574 0.3884788 6.321728 0.0000000 0.0000002 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.18 12C-Pal agriculture No 42.941360 1.4081744 0.2860221 4.049247 0.0000257 0.0165366 28 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Duganella NA
OTU.4420 12C-Pal agriculture No 113.862921 2.3114293 0.3187109 6.468023 0.0000000 0.0000001 28 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Aquabacterium NA
OTU.1863 12C-Ami meadow No 63.905406 1.7674692 0.3457300 4.389175 0.0000057 0.0013464 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.315 12C-Ami meadow No 168.680186 1.4360614 0.2688758 4.411186 0.0000051 0.0013464 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia uncultured bacterium
OTU.66 12C-Van meadow No 45.834871 1.7014041 0.3599458 4.032285 0.0000276 0.0104398 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van meadow No 828.031945 2.7981725 0.2536109 10.047569 0.0000000 0.0000000 4 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.29 12C-Van meadow No 23.315455 1.6340944 0.3734184 3.706551 0.0001051 0.0317673 4 Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Ambiguous_taxa
OTU.85 12C-Van meadow No 50.025534 1.4205890 0.3140419 3.727494 0.0000967 0.0070973 2 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Bacillus NA
OTU.35 12C-Van meadow No 83.736607 1.1173289 0.2567176 3.378533 0.0003644 0.0202832 2 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Bacillus uncultured bacterium
OTU.2977 12C-Van meadow No 43.992439 1.4438190 0.3223333 3.703679 0.0001062 0.0070973 2 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Bacillus Ambiguous_taxa
OTU.98 12C-Van meadow No 26.822922 1.5949465 0.3292538 4.084832 0.0000221 0.0019933 2 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Bacillus Ambiguous_taxa
OTU.2396 12C-Van meadow No 50.033273 2.6333978 0.3383678 7.043808 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia NA
OTU.66 12C-Van meadow No 46.853527 2.5619807 0.3194617 7.237114 0.0000000 0.0000000 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.2 12C-Van meadow No 818.088037 2.2775018 0.3727713 5.438996 0.0000000 0.0000036 2 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Burkholderia-Caballeronia-Paraburkholderia NA
OTU.16149 12C-Van meadow No 12.255583 1.6074319 0.4011831 3.383572 0.0003577 0.0202832 2 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Pseudomonas koreensis
OTU.9 12C-Ami forest No 279.245634 1.2881788 0.1957360 5.303976 0.0000001 0.0000441 2 Bacteria Actinobacteria Actinobacteria Micrococcales Micrococcaceae Ambiguous_taxa Ambiguous_taxa